How did we get here? And what’s next?

Do you remember the days of earnestly debating whether online learning was possible? When posting slides or using clickers made you an ed tech whiz, a lone innovator exploring the frontiers of technology-enhanced learning?

I do, and it’s stunning how different the scene is today. Tech is mainstream within higher education, and increasingly, within K-12 too. It’s also big business, with new ed tech ventures being launched faster than we can count. Of the deluge of products these ventures are pumping out, some are promising and many miss the mark – but quality issues aside, there is clearly a frenzy of development on a scale we’ve never seen before.

This gives us a vast array of tools to pick from, and thus a growing need to make informed decisions about the tech we use to engage today’s students. I don’t believe that the way people learn has really changed (even for so-called digital natives). We humans are walking around with essentially the same brains we had back on the savannah, and our basic psychology reflects that fact more than the influence of our modern tech-saturated environment.

What has changed is our understanding of that basic psychology, thanks to rapid advances in the science of learning. Used in the right way, these discoveries can help us cut through the hype and move toward realizing the promise of ed tech as it was sold to us from the outset: more learning, for more people, in more times and places than we’ve ever seen before.

Advances in technology, expansion of learning science, upheaval in the organization and financing of higher education – all of these have a profound impact on what it’s like to work in the field of learning. Those of us in academia have seen for ourselves (or have been repeatedly informed by our local administrators) that change is coming, but it’s hard to know what that means beyond “more tech and less money.” And in this context of vaguely understood but rapid change, there is sometimes a disconnect between technology development and academia, reflected in products that don’t really address the pressing institutional needs of the day.

Nobody knows what educational technology, or education itself, is going to look like a few years from now. But there are three big areas of movement that are pretty hard to miss:

Trend 1: Learning that lives outside of courses. There isn’t any reason, from a learning science perspective, to privilege the traditional semester-long package of content. There’s also growing interest in finding other ways to document learning besides the traditional “X number of course credits” approach. Badging and competency based learning are high-profile examples that have generated a wild flurry of interest in the last few years. However, these may actually be manifestations of a deeper shift toward meeting learners’ goals in a context of infinitely flexible options.

In the aftermath of MOOC-mania, there was debate about low completion rates and what those mean. Some of those low rates probably reflect lack of realistic understanding of the time and effort involved in completing a college-level course (especially in the stripped-down, low-support MOOC environment). But they may also reflect learner desires for only part of the course. As some commenters have pointed out, MOOC users may come in, figure out what they want to get from the full course, do just that part and leave.

With this type of grab-and-go learning becoming more the norm, it will be harder and harder to say what is “higher education” and what is continuing education for professional growth – i.e., job training. E-learning companies have been in the corporate training game for a while, but there is still more room for growth for these resources to adopt best practices gleaned from learning science. With access in the hands of learners, this training also looks less like something your employer strong-arms you into doing. Rather, individuals will begin to steer themselves into learning experiences as a way of building their own portfolios of selected competencies and areas of content expertise.

On the faculty side, as open and shareable resources continue to gain traction, we may see more instructors incorporating things like freestanding modules into their courses, and conversely, more people investing effort into making and disseminating them. That’s been my experience with the Attention Matters module I created with my collaborators John Doherty and Rick McDonald. Attention Matters attempts to teach just one thing, and to do so across a wide variety of contexts and potential student populations.

Trend 2: Demand for new learning tools that don’t look anything like what we have now. I’ve called this the “ABS rule” – anything but slideshows. In fairness, slideshows are a solid way to convey certain kinds of content knowledge, and perhaps their persistence as an online presentation format reflects some level of effectiveness.

We can do a lot more than show slides, though, with the technology we have. We also need to be aware of a bigger mindset shift that is slowly taking hold. More and more instructors are reorienting their teaching, in a transition that University of Arizona chemistry professor John Pollard calls “teaching students how we think, not what we know.” Content knowledge complements the ability to think and work in a discipline, and knowing more about an area helps you apply critical thinking and to solve problems. But you don’t develop those thinking skills without practice, and slideshows aren’t especially conducive to practice.

People tend to nod along with this idea, but what stands in the way is the added effort it takes to get away from this familiar, comfortable format and into something that’s sometimes hard to envision, let alone actualize. I don’t think it’s new technology per se that’s the missing piece here. Some of it is just better use of what we already have, or bumping up the quality of existing resources like textbook ancillaries, textbook-linked websites and adaptive quizzing systems.

Similarly, exploiting existing instructional design infrastructure will help us create the collaborations that will support the added effort involved in these new mechanisms for learning. At this point, most institutions have some type of resources set up to support faculty in designing and using instructional technology. Yet sometimes instructors – steeped as we are in the individualistic, lone scholar ethos of faculty culture – don’t seek out the help that is there. Or, fearing that we’ll be opening ourselves up to a barrage of criticism, or a takeover of the course by outsiders to the discipline, we actively avoid it.

It’s up to leadership to figure out how to set up institutional cultures that foster SME-ID collaborations, and those solutions will look different for different places. Yet, it’s striking that there seems to be such a parallel with the technology – it’s not that we don’t have the tools, it’s that we just haven’t gotten into the habit of fully exploiting what’s there.

Trend 3: Analytics-based teaching. Candace Thille, mastermind of the Open Learning Initiative, said it best: Data generated by online learning systems “makes learning visible.” No longer just a byproduct, these numbers – times, scores, patterns of usage and accuracy over time – tell us a story about exactly what individual learners are accomplishing and what interventions might be the most helpful at any given moment.

Or rather, they would tell us, if we only looked at them. In my experience, these data are used minimally or not at all by rank and file faculty. Sometimes this comes from a philosophical objection to boiling learning down to numbers, but the commoner problem is simply not being in the habit of incorporating analytics into the daily flow of teaching. We teachers learn other ways of monitoring progress and giving feedback – flagging low test scores, checking for understanding during class, commenting on written work – but keeping tabs on learning analytics just isn’t part of the routine.

We scholars of teaching have worked to get faculty to take learning theory to heart, and now we see that applying these theories has become mainstream. The time is ripe to do something similar with learning analytics. And just like with learning theory, it’s not sufficient simply to tell faculty this is something they have to tack on to existing practice. Real lasting change will only come about if we win them over philosophically – the “why” part – while also offering concrete examples of the practices involved – the “how” part.

This is why I called this trend “analytics-based teaching,” not “learning analytics.” The latter is a nice buzzword, but emphasizes the data itself, and by extension the software tools for providing that data. At this point in the game, it’s not really a software problem, it’s a practice problem – one that’s going to be resolved by how we teach, not by the tools we buy.

It’s not a practice problem that will be solved with a couple of tip sheets or faculty development days, either. Right now, it’s hard to even explain to colleagues what learning analytics are, let alone how to use them in the courses we teach right now. Frameworks like Michael Feldstein’s taxonomy are a good start, but we need better communication strategies that get people thinking about how they can start taking advantage of learning data, today.

This last trend leads to an even bigger truth that leaders, in particular, need to know: The future will belong to those who can build faculty buy-in. This doesn’t just mean wearing down resistors, but really getting faculty excited to try the new thing. It’s going to be rough going for institutions whose leaders’ persuasive repertoire consists of either drowning people in data or mandating practice from the top down. Cultures that sideline instructional design and e-learning professionals – excluding them from the real work of designing courses – are also going to have a very tough time of it. So how exactly do we go about repairing these institutional problems?

In the next post, I’ll share what to do and not to do to as you work to create powerful alliances with faculty across an institution. What do you think of these three trends – and what’s still missing?